The baseline implementations are directly taken from their public code repositories (after necessary adaptations to work with the Python based environment):
IFhttps://github.com/tharindurb/iNNE, DILOFhttp://di.postech.ac.kr/DILOF/, CAE-M (with CNN Slimminghttps://github.com/liuzhuang13/slimming), USADhttps://github.com/manigalati/usad (with model compressionhttps://github.com/antspy/quantized_distillation), MAD-GANhttps://github.com/LiDan456/MAD-GANs (with GAN slimminghttps://github.com/VITA-Group/GAN-Slimming) and SlimGANhttps://github.com/houliangict/SlimGAN. Other models were developed by us. For training we used learning rate of \(10^{-4}\)  with weight decay of \(10^{-5}\) . We used Adam optimizer with CosineAnnealing learning rate decay with warm restarts every 10 epochs. For POT parameters, \(\mathrm {coefficient} = 10^{−4}\)  for all data sets, low quantile is 0.07 for FTSAD-1/25/55, 0.01 for SMD, and 0.001 for MSDS. These are selected as per [1]}.
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